DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This non-final office action is in response to the application and preliminary amendment filed 16 April 2024.
Claims 1-15 are pending. Claims 1, 14, and 15 are independent claims.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 16 April 2024 and 27 February 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The examiner accepts the drawings filed 16 April 2024.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a light guide unit that guides… (claim 1, lines 5-7)”
“a light receiving unit that receives… (claim 1, lines 8-9)”
“a control unit that detects… (claim 1, lines 10-12)”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 5, the term “mainly (line 4)” is a relative term which renders the claim indefinite. The term “mainly” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
With respect to independent claim 15, the claim recites a “program causing a neural network device to perform processing (lines 1-2).” A program fails to device a process, machine, manufacture, or composition of matter, and amounts to software per se. For this reason, claim 16 is non-statutory.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 8, and 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. (Diffractive Deep Neural Networks at Visible Wavelengths, 13 February 2021, hereafter Chen).
As per independent claim 1, Chen discloses a neural network device, comprising:
one or a plurality of optical diffractive deep neural networks each optimized for light of a predetermined wavelength region (Sections 1 and 3: Here, a diffractive deep neural network (D2NN) is defined. The D2NN is fabricated as five layers of diffractive optical elements and trained as a digit classifier to perform automated classification of handwritten digits)
a light guide unit that guides light of an optimized wavelength region to the optical diffractive deep neural network (Figure 1: Here, a He-Ne laser is directed through lenses 1 and 2 with a pinhole acting as a filter (Section 3). The lenses and pinholes are analogous to the light guide unit)
a light receiving portion that receives light output from the optical diffractive deep neural network (Figure 1: Here, an output plane and charge coupled device (CCD) receive output from the D2NN (Section 3))
a control unit that detects a target object on a basis of a signal corresponding to the light received by the light receiving portion (Figure 3; Section 3: Here, a target object, a handwritten digit, is detected on the basis of the signal)
As per dependent claim 2, Chen discloses wherein the optical diffractive deep neural network includes a plurality of optical diffractive deep neural networks that is optimized for light of mutually different wavelength regions (Sections 2 and 3: Here, the D2NN contains five layers. The examiner interprets each of these layers as an optical diffractive deep neural network. These layers each contain 1 million neurons corresponding pixels (Section 1). In this specific example, the D2NN is optimized for light from a He-Ne laser (Section 3), visible light, and/or near-infrared wavelengths (Section 1)).
As per dependent claim 8, Chen discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Chen discloses wherein the light guide unit is an irradiation unit that is able to emit light of a predetermined wavelength region (Section 3: Here, the He-Ne laser is able to produce light in a wavelength of 632.8 nm. Further, the light may be produced as visible light).
With respect to claims 14-15, the claims recite the limitations substantially similar to those in claim 1. Claim 14-15 are rejected under substantially similar rationale.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-7 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and further in view of Jang et al. (US 2020/0184624, published 11 June 2020, hereafter Jang).
As per dependent claim 3, Chen discloses the limitations similar to those in claim 1 and the same rejection is incorporated herein. Chen fails to specifically disclose wherein the light guide unit is a spectroscopic portion that disperses light.
However, Jang, which is analogous to the claimed invention because it is directed toward dispersing light, discloses wherein the light guide unit is a spectroscopic portion that disperses light (paragraph 0067: Here, a spectroscopic element is used to divide light into wavelength bands to disperse the divided light). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed for dividing light into appropriate wavelength bands (Jang: paragraph 0067) for use with the D2NN (Chen: Section 1).
As per dependent claim 4, Chen and Jang disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Jang discloses wherein the spectroscopic portion guides light of an optimized wavelength among the dispersed light (paragraph 0067: Here, a spectroscopic element is used to divide light into wavelength bands to disperse the divided light). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed for dividing light into appropriate wavelength bands (Jang: paragraph 0067) for use with the D2NN (Chen: Section 1).
As per dependent claim 5, Chen and Jang disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Chen discloses:
the optical diffractive deep neural network optimized for light of a wavelength region (Sections 1 and 3: Here, a diffractive deep neural network (D2NN) is defined. The D2NN is fabricated as five layers of diffractive optical elements and trained as a digit classifier to perform automated classification of handwritten digits)
the light receiving portion receives light emitted from the optical diffractive deep neural network optimized for light of the wavelength region mainly reflected by the target object (Figure 1: Here, an output plane and charge coupled device (CCD) receive output from the D2NN (Section 3))
the control unit detects the target object on a basis of a signal input from the light receiving portion that has received light emitted from the optical diffractive deep neural network optimized for light of the wavelength region mainly reflected by the target object (Figure 3; Section 3: Here, a target object, a handwritten digit, is detected on the basis of the signal)
Chen fails to specifically disclose a wavelength region mainly reflected by the target object. However, Jang, which is analogous to the claimed invention because it is directed toward dispersing light, discloses the optical diffractive wavelength region mainly reflected by the target object (paragraph 0067: Here, a spectroscopic element is used to divide light into wavelength bands to disperse the divided light). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed for dividing light into appropriate wavelength bands (Jang: paragraph 0067) for use with the D2NN (Chen: Section 1).
As per dependent claim 6, Chen and Jang disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Jang further discloses wherein the spectroscopic portion is a prism (paragraph 0031). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed dispersing the light into a plurality of wavelengths (Jang: paragraph 0031).
As per dependent claim 7, Chen and Jang disclose the limitations similar to those in claim 3, and the same rejection is incorporated herein. Jang further discloses wherein the spectroscopic portion is a diffraction grating (paragraph 0031). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed dispersing the light into a plurality of wavelengths (Jang: paragraph 0031).
As per dependent claim 9, Chen discloses the limitations similar to those in claim 8, and the same rejection is incorporated herein. Chen discloses wherein:
the irradiation unit is able to emit light (Section 3)
the optical diffractive deep neural network is optimized for each of the plurality of wavelength regions that is able to be emitted by the irradiation unit (Sections 2 and 3: Here, the D2NN contains five layers. The examiner interprets each of these layers as an optical diffractive deep neural network. These layers each contain 1 million neurons corresponding pixels (Section 1). In this specific example, the D2NN is optimized for light from a He-Ne laser (Section 3), visible light, and/or near-infrared wavelengths (Section 1))
Chen fails to specifically disclose wherein the irradiation unit is able to emit light of a plurality of wavelength. However, Jang, which is analogous to the claimed invention because it is directed toward dispersing light, discloses an irradiation unit able to emit light of a plurality of wavelengths (paragraphs 0021 and 0067: Here, an irradiation unit irradiates light from a light source. The spectroscopic element is used to divide light into wavelength bands to disperse the divided light). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed for dividing light into appropriate wavelength bands (Jang: paragraph 0067) for use with the D2NN (Chen: Section 1).
As per dependent claim 10, Chen and Jang disclose the limitations similar to those in claim 9, and the same rejection is incorporated herein. Chen discloses wherein:
the optical diffractive deep neural network is optimized for light of a wavelength region (Sections 1 and 3: Here, a diffractive deep neural network (D2NN) is defined. The D2NN is fabricated as five layers of diffractive optical elements and trained as a digit classifier to perform automated classification of handwritten digits)
the control unit detects the target object on a basis of a signal input from the light receiving portion that has received light emitted from the optical diffractive deep neural network optimized for light of the wavelength region by the target object (Figure 3; Section 3: Here, a target object, a handwritten digit, is detected on the basis of the signal)
Chen fails to specifically disclose the irradiation unit emits light of a wavelength region mainly reflected by a target object to be detected. However, Jang, which is analogous to the claimed invention because it is directed toward dispersing light, discloses reflection by a target object to be detected (paragraphs 0021-0022 and 0067: Here, an irradiation unit irradiates light from a light source and uses a mirror and condensing lens to focus light. Light is reflected off of the inspection object and gathered). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Jang with Chen, with a reasonable expectation of success, as it would have allowed for dividing light into appropriate wavelength bands (Jang: paragraph 0067) for use with the D2NN (Chen: Section 1).
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and further in view of Galor Gluskin et al. (US 2023/0080715, filed 16 September 2021, hereafter Galor Gluskin).
As per dependent claim 11, Chen discloses the limitations similar to those in claim 1, and the same rejection is incorporated herein. Chen discloses an imaging control unit that controls an imaging element to start imaging (Figure 3; Section 3: Here, a target object, a handwritten digit, is detected on the basis of the signal).
Chen fails to specifically disclose start imaging with detection of the target object as the trigger. However, Galor Gluskin, which is analogous to the claimed invention because it is directed toward image sensors, discloses start imaging with the detection of the target object as the trigger (paragraph 0150: Here, a request is triggered automatically by a detection of one or more objects by the image sensor). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Galor Gluskin, with a reasonable expectation of success, as it would have allowed for triggering the diffractive deep neural network of Chen based upon detecting an object by the image sensor (Galor Gluskin: paragraph 0150).
As per dependent claim 12, Chen and Galor Gluskin disclose the limitations similar to those in claim 11, and the same rejection is incorporated herein. Chen discloses wherein the imaging element and the optical diffractive deep neural network are stacked (Figure 3).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Chen and Galor Gluskin and further in view of Zhou et al. (US 11893482, filed 24 February 2020, hereafter Zhou).
As per dependent claim 13, Chen and Galor Gluskin disclose the limitations similar to those in claim 11, and the same rejection is incorporated herein. Chen discloses wherein the imaging element receives visible light and the optical diffractive neural network receives visible light (Figure 1; Section 3).
Chen fails to specifically disclose receiving infrared light having passed through the imaging element. However, Zhou, which is analogous to the claimed invention because it is directed toward a neural network using infrared image sensors, discloses receiving infrared light having passed through the imaging element (column 4, line 63- column 5, line 16). It would have been obvious to one of ordinary skill in the art at the time of the applicant’s effective filing date to have combined Zhou with Chen-Galor Gluskin, with a reasonable expectation of success, as it would have allowed for using wavelengths of infrared light to fill in missing portions to generate a more complete representation (Zhou: column 1, lines 25-36).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Lin et al. (All-optical machine learning using diffractive deep neural networks, 7 September 2018): Discloses diffractive deep neural network using diffractive layers that work collectively to classify images and fashion products (Abstract)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE R STORK whose telephone number is (571)272-4130. The examiner can normally be reached 8am - 2pm; 4pm - 6pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas can be reached at 571/272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KYLE R STORK/Primary Examiner, Art Unit 2128
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